Volume 42 | Number 3p1 | June 2007

Abstract List

Susan E. Stockdale, Lingqi Tang, Lily Zhang, Thomas R. Belin, Kenneth B. Wells


This study adapts Andersen's Behavioral Model to determine if health sector market conditions affect vulnerable subgroups' use of alcohol, drug, and mental health services (ADM) differently than the general population, focusing specifically on community‐level predisposing and enabling characteristics.

Data Sources

Wave 2 data (2000–2001) from the Health Care for Communities study, supplemented with cases from wave 1 (1997–1998), were merged with area characteristics taken from Census, Area Resource File (ARF), and other data sources.

Study Design

The study used four‐level hierarchical logistic regression to examine access to ADM care from any provider and specialty ADM access. Interactions between community‐level predisposing and enabling vulnerability characteristics with individual race/ethnicity, age, income category, and insurance type were explored.

Principal Findings

Nonwhites, the poor, uninsured, and elderly had lower likelihoods of service use, but interactions between race/ethnicity, income, age and insurance status with community‐level vulnerability factors were not statistically significant for any service use. For ADM specialty care, those with Medicare, Medicaid, private fully managed, and private partially managed insurance, the likelihood of utilization was higher in areas with higher HMO penetration. However, for those with other insurance or no insurance plan, the likelihood of utilization was lower in areas with higher HMO penetration.


Community‐level enabling factors explain part of the effect of disadvantaged status but, with the exception of the effect of HMO penetration on the relationship between insurance and specialty care use, do not modify any of the residual individual‐level effects of disadvantage. Interventions targeting both structural and individual levels may be necessary to address the problem of health disparities. More research with longitudinal data is necessary to sort out the causal direction of social context and ADM access outcomes, and whether policy interventions to change health sector market conditions can shift ADM treatment utilization.